Article
Computer Science, Information Systems
Kai Zhang, Chaonan Shen, Juanjuan He, Gary G. Yen
Summary: The proposed MMO-EvoKnee algorithm incorporates MCDM strategy to efficiently search for a complete set of global knee solutions for MMOPs. It outperforms existing state-of-the-art MMOEAs and provides decision makers with well-converged alternative solutions.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Xinli You, Fujun Hou
Summary: Consensus-reaching process (CRP) is crucial in group decision-making (GDM) and involves the dynamic interaction between decision makers (DMs) and moderators. To model this interaction, an evolutionary game theory-based consensus model is proposed. The model includes strategy stability analysis and the development of a multi-objective programming consensus model. A genetic algorithm is designed to obtain the Pareto solution set. Sensitivity analysis is conducted to improve consensus, highlighting the importance of the evolutionary game between DMs and moderators in CRP.
INFORMATION FUSION
(2023)
Article
Polymer Science
Antonio Gaspar-Cunha, Paulo Costa, Alexandre Delbem, Francisco Monaco, Maria Jose Ferreira, Jose Covas
Summary: Polymer single-screw extrusion is a vital industrial technique for obtaining plastic products. The design characteristics of extruder screws can vary to achieve high outputs and excellent product performance. Barrier screws, with an additional flight in the compression zone, have gained popularity due to their ability to enhance and stabilize polymer melting. Thus, designing efficient extruder screws and selecting between conventional and barrier screws are crucial considerations.
Article
Computer Science, Artificial Intelligence
Derya Deliktas, Ender Ozcan, Ozden Ustun, Orhan Torkul
Summary: The study introduces evolutionary algorithms to solve the bi-objective flexible job shop scheduling problem and compares their performance across various configurations. The transgenerational memetic algorithm using weighted sum method outperforms others and achieves the best-known results for almost all instances of bi-objective flexible job shop cell scheduling.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Jesus Guillermo Falcon-Cardona, Raquel Hernandez Gomez, Carlos A. Coello Coello, Ma. Guadalupe Castillo Tapia
Summary: This paper presents a survey of parallel implementations of multi-objective evolutionary algorithms (pMOEAs), discussing their significance in tackling computationally expensive applications, describing taxonomy and methods review, and proposing open questions for further research.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Edgar Galvan, Fergal Stapleton
Summary: This study makes progress in neuroevolution for vehicle trajectory prediction by adopting rich artificial neural networks and two evolutionary multi-objective optimization algorithms. The underlying mechanisms and response to objective scaling of each algorithm are revealed. Additionally, certain objectives are found to be beneficial while others are detrimental to finding valid models.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Yanping Wang, Yuan Liu, Juan Zou, Jinhua Zheng, Shengxiang Yang
Summary: Balancing convergence and diversity in constrained multi-objective optimization problems is challenging. Existing evolutionary algorithms are insufficient, hence a novel algorithm named DTAEA is proposed. DTAEA divides the population's evolutionary process into two phases to improve exploration capability and guide population distribution in feasible regions.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Automation & Control Systems
Pengcheng Jiang, Yu Xue, Ferrante Neri
Summary: Dropout is an effective method for training deep neural networks by deactivating some neurons to mitigate overfitting. This paper proposes a novel approach to guide the dropout rate using an evolutionary algorithm, allowing for more flexibility in training. Experimental results demonstrate that this method consistently outperforms other dropout methods, including state-of-the-art techniques.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Yongkuan Yang, Pei-Qiu Huang, Xiangsong Kong, Jing Zhao
Summary: This paper proposes a novel constrained multi-objective evolutionary algorithm called CMAOO, which optimizes an (M+1)-objective optimization problem consisting of the original M objective functions and the degree of constraint violation. It constructs a main population and saves all feasible solutions in an external archive. The main population and the external archive are evolved to search the whole space and the feasible regions, respectively, and their offspring update the external archive and the main population separately. Experimental studies show that CMAOO is competitive in solving constrained multi-objective optimization problems compared to four state-of-the-art algorithms.
APPLIED SOFT COMPUTING
(2023)
Editorial Material
Computer Science, Artificial Intelligence
Xingyi Zhang, Ran Cheng, Liang Feng, Yaochu Jin
Summary: Optimization and learning are two main paradigms of artificial intelligence, frequently enhanced by each other in addressing complex real-world problems. Evolutionary multi-objective optimization algorithms are widely used but face challenges in solving complex problems. Machine learning techniques have been applied to enhance these algorithms.
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
(2023)
Article
Computer Science, Artificial Intelligence
Xiangsong Kong, Yongkuan Yang, Zhisheng Lv, Jing Zhao, Rong Fu
Summary: This paper proposes a dynamic dual-population co-evolution multi-objective evolutionary algorithm (DDCMEA) to address the issue of balancing feasibility, convergence, and diversity in constrained multi-objective optimization problems. DDCMEA employs a dynamic dual-population co-evolution strategy to balance convergence and feasibility by adjusting the offspring number of the two populations. In the early stage, the algorithm focuses on convergence and generates more offspring of the first population, while in the late stage, it focuses on feasibility and generates more offspring of the second population. The results show that DDCMEA achieves competitive performance in handling constrained multi-objective optimization problems.
APPLIED SOFT COMPUTING
(2023)
Article
Management
Konstantinos Liagkouras, Konstantinos Metaxiotis
Summary: The paper explores the importance of individual behaviors in evolutionary algorithms and introduces a novel approach that generates individuals with different reactions to the same stimulus, inspired by human social interactions. The proposed method outperforms other state-of-the-art algorithms in various test instances, showcasing the significance of incorporating diverse behaviors in evolutionary algorithms.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Engineering, Electrical & Electronic
Jiguang Yang, Jiuyuan Huo, Hamzah Murad Mohammed Al-Neshmi
Summary: The ETC algorithm proposed in this paper aims to form Field Observation Instruments Networks (FOIN) and accelerate the general automation rate as well as real-time data exchange in field observation. The algorithm can select the optimal cluster head (Optimal-CH) through multi-objective decision-making and enhances energy conservation in FOIN.
IEEE SENSORS JOURNAL
(2021)
Article
Computer Science, Information Systems
Yi Xiang, Jinhua Zheng, Yaru Hu, Yuan Liu, Juan Zou, Qi Deng, Shengxiang Yang
Summary: This paper proposes a multimodal multi-objective algorithm based on weak relationship indicators, which allows the population to retain solutions from different Pareto sets during exploration. An archive based on weak convergence indicators is also introduced to retain excellent solutions.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Theory & Methods
Raquel Espinosa, Fernando Jimenez, Jose Palma
Summary: Air pollution forecasting modeling is crucial for improving air quality, ecosystems, and human health. This paper presents a novel spatio-temporal approach based on multi-objective evolutionary algorithms for air pollution forecasting. The proposed method, which identifies and combines multiple non-dominated linear regression models, has achieved promising results in predicting NO2 levels in southeastern Spain.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Interdisciplinary Applications
F. Mallor, C. Azcarate, R. Blanco, P. Mateo
JOURNAL OF SIMULATION
(2015)
Article
Computer Science, Artificial Intelligence
B. Lacruz, D. Lahoz, P. M. Mateo
Article
Computer Science, Artificial Intelligence
P. M. Mateo, I. Alberto
APPLIED SOFT COMPUTING
(2018)
Article
Computer Science, Information Systems
Isolina Alberto, Carlos A. Coello Coello, Pedro M. Mateo
INFORMATION SCIENCES
(2014)
Article
Computer Science, Artificial Intelligence
David Lahoz, Beatriz Lacruz, Pedro M. Mateo
Article
Engineering, Multidisciplinary
Francisco Ballestin, Fermin Mallor, Pedro M. Mateo
OPTIMIZATION AND ENGINEERING
(2012)
Article
Operations Research & Management Science
I. Alberto, P. M. Mateo
Article
Economics
Ivan Carlo Alberto, Yang Jiao, Xiaohan Zhang
Summary: This study investigates the impact of temperature on students' time allocation, finding that students adjust study time for leisure in extreme temperatures. The response to weather varies between college and high school students, with the latter showing more frequent reductions in study time during cold days. Students in climates different from what they are accustomed to are observed to react more strongly to temperature changes.
ECONOMICS OF EDUCATION REVIEW
(2021)
Article
Computer Science, Artificial Intelligence
P. M. Mateo, D. Lahoz, I. Alberto
Summary: Non-Dominated Sorting process (NDS) is crucial in Pareto-based evolutionary multi-objective optimization algorithms, especially in steady-state evolutionary algorithms where the Pareto layers need to be updated for each new solution. This paper presents a general framework and three implementations based on a modified Irreducible Domination Graph structure (IDG) to carry out the NDS process efficiently. The proposed algorithms are compared with other NDS algorithms specifically designed for incremental updates of Pareto layers, showing reduced time and Pareto comparisons.
APPLIED SOFT COMPUTING
(2022)
Article
Green & Sustainable Science & Technology
Cristina Azcarate, Fermin Mallor, Pedro Mateo